GitHub repository that we used for the API call functions:
import requests
import re
import json
import pandas as pd
import numpy as np
import matplotlib.lines as mlines
from itertools import chain
from matplotlib import pyplot as plt
import os
# Making the get request to the Fair-Checker API that includes checking for all available metrics
# If the call is successful it stores the result and status code 200, else it stores None and 504
def get_all_metrics (payload):
global count
print(count)
FAIR_checker_api = "https://fair-checker.france-bioinformatique.fr/api/check/metrics_all"
try:
response = requests.get(FAIR_checker_api, params=payload)
print(payload)
print(response.status_code)
print("________________")
count += 1
return response.json(), response.status_code
except:
status = 504
print(payload)
print(status)
print("________________")
return None, status
# Transforms the result from the API call into a JSON dictionary
def build_assessment_json(assessment):
regex_result = re.findall("'metric': '.*?', 'score': '.*?'", assessment)
assessment_list = []
for result in regex_result:
result = result.replace("\'", "\"")
assessment_dict = json.loads("{"+result+"}")
assessment_list.append(assessment_dict)
return assessment_list
# Prints the most basic summary statistics about the 'Score' column
def print_summary_stats(df):
print("Mean: " + str(df["Score"].mean()))
print("Median: " + str(df["Score"].median()))
print("Max: " + str(df["Score"].max()))
print("Min: " + str(df["Score"].min()))
path = r"C:\Users\MolnarM\OneDrive - BDO Austria\Desktop\DSAI2"
os.chdir(path)
input_file_path = r"./ogdmetadatendatagvat.csv"
output_file_path = r"./ogdmetadatendatagvat_cleaned.csv"
# Read the content of the file
with open(input_file_path, 'r') as file:
content = file.read()
# Replace backslashes with forward slashes
modified_content = content.replace("\\", "/")
# Write the modified content back to a new file
with open(output_file_path, 'w') as file:
file.write(modified_content)
df = pd.read_csv(r"./ogdmetadatendatagvat_cleaned.csv", encoding= 'unicode_escape', sep=';', on_bad_lines='skip', header=5, skiprows=[6,7])
df.head(10)
#defining the date (its performed on)
date = "2024-05-14"
count = 0
assessment_results = df['metadata_identifier'].map(lambda x: get_all_metrics(payload={'url': 'https://www.data.gv.at/katalog/dataset/' + x}))
df['FAIR_assessment'] = [aresult[0] for aresult in assessment_results]
df['assessment_status_code'] = [aresult[1] for aresult in assessment_results]
df.to_csv(r"./ogdmetadatendatagvat_assessed_"+date+".csv", encoding='utf-8')
df
import pandas as pd
import requests
import time
# Keeping track of the progress
count = 0
def get_all_metrics(payload):
global count
print(count)
FAIR_checker_api = "https://fair-checker.france-bioinformatique.fr/api/check/metrics_all"
try:
response = requests.get(FAIR_checker_api, params=payload)
print(payload)
print(response.status_code)
print("________________")
count += 1
return response.json(), response.status_code
except:
status = 504
print(payload)
print(status)
print("________________")
return None, status
def assess_and_save(df, save_interval=100, save_path=r"./ogdmetadatendatagvat_assessed_intermediate.csv"):
# Initialize columns with None
df['FAIR_assessment'] = None
df['assessment_status_code'] = None
for index, x in df['metadata_identifier'].iteritems():
if pd.notna(x):
result = get_all_metrics(payload={'url': 'https://www.data.gv.at/katalog/dataset/' + str(x)})
else:
result = (None, None)
# Update the DataFrame with the result
df.at[index, 'FAIR_assessment'] = result[0]
df.at[index, 'assessment_status_code'] = result[1]
# Save intermediate results
if (index + 1) % save_interval == 0:
df.to_csv(save_path, encoding='utf-8', index=False)
print(f"Saved intermediate results at index {index + 1}")
time.sleep(1) # To avoid hitting API rate limits or overloading the server
return df
# Perform the assessment and save intermediate results
df = assess_and_save(df)
# Save the final results
date = "20230527" # Replace with your desired date string
output_path = r"./ogdmetadatendatagvat_assessed_" + date + ".csv"
df.to_csv(output_path, encoding='utf-8', index=False)
# Saving the results as an excel file as well
df = pd.read_excel(r"./assessed_excel.xlsx")
df['FAIR_assessment'][0]
# Only keeping successful assessments
df = df[df["assessment_status_code"] == 200]
df = df.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
# Transforming the results to JSON dicts
df["FAIR_assessment"] = df["FAIR_assessment"].map(lambda x: build_assessment_json(x))
df
| Unnamed: 0 | local_id | metadata_identifier | metadata_modified | Title | description | categorization | tags | maintainer | license | ... | lineage_quality | en_title_and_desc | license_citation | metadata_original_portal | maintainer_email | publisher_link | publisher_email | license_url | terms_url | FAIR_assessment | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 3E7BDC0B-6CBC-5A25-AF07-409E1EA40408 | 2023-07-03 | Entwicklung nachhaltiger Schilferntetechniken ... | NaN | Umwelt | Nationalparks Austria, Naturschutz, OpenDocume... | NP Neusiedler See - Seewinkel | Creative Commons Attribution License 3.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | https://creativecommons.org/licenses/by/3.0/ | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 1 | 1 | 2 | d7e2c489-c233-4458-9d98-a2048a73a43e | 2023-07-02 | Voranschlag Kohfidisch 2023 (Gemeinde) | Voranschlag der Gemeinde - Einnahmen und Ausgaben | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 10908 Kohfidisch | Creative Commons Attribution License 3.0 | ... | Gemeinde | Kohfidisch: Municipal Spending (closed account... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/3.0/ | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 2 | 2 | 3 | 03986e54-6367-41aa-bcba-8c51036237dc | 2023-07-02 | Rechnungsabschluss Kohfidisch 2022 (Gemeinde) | Rechnungsabschluss der Gemeinde - Einnahmen un... | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 10908 Kohfidisch | Creative Commons Attribution License 3.0 | ... | Gemeinde | Kohfidisch: Municipal Spending (closed account... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/3.0/ | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 3 | 3 | 4 | 3e54878c-8657-4268-ad6e-064c3869169e | 2023-07-02 | Rechnungsabschluss Kohfidisch 2021 (Statistik ... | Rechnungsabschluss der Gemeinde - Einnahmen un... | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 10908 Kohfidisch | Creative Commons Attribution License 3.0 | ... | Statistik Austria | Kohfidisch: Municipal Spending (closed account... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/3.0/ | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 4 | 4 | 5 | 0718837e-ffba-4833-b38f-a56b8ce0c012 | 2023-07-02 | Rechnungsabschluss Kohfidisch 2020 (Statistik ... | Rechnungsabschluss der Gemeinde - Einnahmen un... | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 10908 Kohfidisch | Creative Commons Attribution License 3.0 | ... | Statistik Austria | Kohfidisch: Municipal Spending (closed account... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/3.0/ | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 14132 | 14144 | 14145 | 3fe4b483-0a4f-4dab-94ed-076fe22b770b | 2022-01-10 | Voranschlag Pöndorf 2021 (Gemeinde) | Voranschlag der Gemeinde - Einnahmen und Ausgaben | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 41726 Pöndorf | Creative Commons Namensnennung 4.0 International | ... | Gemeinde | Pöndorf: Municipal Spending (closed accounts) ... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/4.0/de... | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 14133 | 14145 | 14146 | f21ab3d9-ba40-461f-9576-52285a030a31 | 2022-01-10 | Rechnungsabschluss Pöndorf 2020 (Gemeinde) | Rechnungsabschluss der Gemeinde - Einnahmen un... | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 41726 Pöndorf | Creative Commons Namensnennung 4.0 International | ... | Gemeinde | Pöndorf: Municipal Spending (closed accounts) ... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/4.0/de... | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 14134 | 14146 | 14147 | 36aa0889-39cd-4d4b-b17b-130e53af4c4a | 2022-01-10 | Rechnungsabschluss Mauthausen 2020 (Gemeinde) | Rechnungsabschluss der Gemeinde - Einnahmen un... | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 41111 Mauthausen | Creative Commons Namensnennung 4.0 International | ... | Gemeinde | Mauthausen: Municipal Spending (closed account... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/4.0/de... | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 14135 | 14147 | 14148 | 05269861-a62f-4b95-8bc1-43a778984208 | 2022-01-10 | Rechnungsabschluss Purgstall an der Erlauf 202... | Rechnungsabschluss der Gemeinde - Einnahmen un... | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 32008 Purgstall an der Erlauf | Creative Commons Namensnennung 4.0 International | ... | Gemeinde | Purgstall an der Erlauf: Municipal Spending (c... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/4.0/de... | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 14136 | 14148 | 14149 | 36cbb257-de7d-484a-80e2-209177977117 | 2022-01-10 | Voranschlag Aschach an der Steyr 2021 (Gemeinde) | Voranschlag der Gemeinde - Einnahmen und Ausgaben | Wirtschaft und Finanzen | Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... | 41502 Aschach an der Steyr | Creative Commons Namensnennung 4.0 International | ... | Gemeinde | Aschach an der Steyr: Municipal Spending (clos... | Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... | NaN | NaN | https://www.offenerhaushalt.at | NaN | https://creativecommons.org/licenses/by/4.0/de... | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
14137 rows × 33 columns
# Converting the metrics to a dictionary and creating a seperate df out of it
def convert_metrics_to_dict(metric_list):
return {d['metric']: d['score'] for d in metric_list}
# Apply the function to the 'metrics' column
metrics_dicts = df['FAIR_assessment'].apply(convert_metrics_to_dict)
# Create a new DataFrame from the list of dictionaries
metrics_df = pd.DataFrame(metrics_dicts.tolist())
metrics_df
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 1 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 2 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 3 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 4 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 14132 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 14133 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 14134 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 14135 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 14136 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
14137 rows × 12 columns
# Combining the metadata information and the table that contains the scores for the metrics
df_combined = pd.concat([df, metrics_df], axis=1, ignore_index=True)
df_combined = df_combined.drop(columns = [0,1])
df_combined.columns
df_combined.loc[:, 33:]
| 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 1 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 2 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 3 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 4 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 14132 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 14133 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 14134 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 14135 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
| 14136 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 1 |
14137 rows × 12 columns
columns = df_combined.columns
# Lost the column names in the meantime so renaming them so we now which column corresponds to which metric
new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']
# Renaming the last 12 columns - the ones containing the metrics
df_combined.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)
print(df_combined.head())
2 3 \
0 3E7BDC0B-6CBC-5A25-AF07-409E1EA40408 2023-07-03
1 d7e2c489-c233-4458-9d98-a2048a73a43e 2023-07-02
2 03986e54-6367-41aa-bcba-8c51036237dc 2023-07-02
3 3e54878c-8657-4268-ad6e-064c3869169e 2023-07-02
4 0718837e-ffba-4833-b38f-a56b8ce0c012 2023-07-02
4 \
0 Entwicklung nachhaltiger Schilferntetechniken ...
1 Voranschlag Kohfidisch 2023 (Gemeinde)
2 Rechnungsabschluss Kohfidisch 2022 (Gemeinde)
3 Rechnungsabschluss Kohfidisch 2021 (Statistik ...
4 Rechnungsabschluss Kohfidisch 2020 (Statistik ...
5 6 \
0 NaN Umwelt
1 Voranschlag der Gemeinde - Einnahmen und Ausgaben Wirtschaft und Finanzen
2 Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen
3 Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen
4 Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen
7 \
0 Nationalparks Austria, Naturschutz, OpenDocume...
1 Ausgaben, Budget, Einnahmen, Ergebnishaushalt,...
2 Ausgaben, Budget, Einnahmen, Ergebnishaushalt,...
3 Ausgaben, Budget, Einnahmen, Ergebnishaushalt,...
4 Ausgaben, Budget, Einnahmen, Ergebnishaushalt,...
8 9 \
0 NP Neusiedler See - Seewinkel Creative Commons Attribution License 3.0
1 10908 Kohfidisch Creative Commons Attribution License 3.0
2 10908 Kohfidisch Creative Commons Attribution License 3.0
3 10908 Kohfidisch Creative Commons Attribution License 3.0
4 10908 Kohfidisch Creative Commons Attribution License 3.0
10 11 ... F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 \
0 2022-04-01 Nationalparks Austria ... 1 1 2 2 1 1 0 2
1 2023-01-01 Offenerhaushalt.at ... 1 1 2 2 1 1 0 2
2 2022-01-01 Offenerhaushalt.at ... 1 1 2 2 1 1 0 2
3 2021-01-01 Offenerhaushalt.at ... 1 1 2 2 1 1 0 2
4 2020-01-01 Offenerhaushalt.at ... 1 1 2 2 1 1 0 2
R1.2 R1.3
0 2 1
1 2 1
2 2 1
3 2 1
4 2 1
[5 rows x 43 columns]
# Converting the metric columns to numerical
for col in new_column_names:
df_combined[col] = pd.to_numeric(df_combined[col], errors='coerce')
# Summing the last 12 columns row-wise and storing the result in a new column called 'Score'
df_combined['Score'] = df_combined[new_column_names].sum(axis=1)
# First look at the results
print_summary_stats(df_combined)
Mean: 14.021220909669662 Median: 15.0 Max: 19 Min: 0
# Removing the limit so all the columns are displayed
pd.set_option('display.max_columns', None)
df2 = pd.read_csv(r"./ogdmetadatendatagvat_assessed_german2024-06-10.csv", index_col = [0])
df2.head()
| author | author_email | creator_user_id | id | isopen | license_id | license_title | maintainer | maintainer_email | metadata_created | ... | url | version | extras | groups | resources | tags | relationships_as_subject | relationships_as_object | FAIR_assessment | assessment_status_code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Hanse- und Universitätsstadt Rostock – Hauptamt | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 05be0aa5-76a2-4850-b37f-69e623565fbe | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2023-01-06T06:14:11.920576 | ... | https://www.opendata-hro.de/dataset/e7c10dd4-d... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Regierun... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'politik', 'id': 'a8e6e4aa-6... | [] | [] | [{'metric': 'F1A', 'score': '2', 'target_uri':... | 200 |
| 1 | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 44fa7d61-7924-4740-95ec-b4a6821139fe | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2024-05-08T06:31:53.940777 | ... | https://www.opendata-hro.de/dataset/c276228b-9... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Regierun... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'bauleitplanung', 'id': '861... | [] | [] | [{'metric': 'F1A', 'score': '2', 'target_uri':... | 200 |
| 2 | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 6219e4c3-e819-40f0-86cd-543ff829ad6c | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2024-05-08T06:31:46.141017 | ... | https://www.opendata-hro.de/dataset/655f46c2-1... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Regierun... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'bauleitplanung', 'id': '861... | [] | [] | [{'metric': 'F1A', 'score': '2', 'target_uri':... | 200 |
| 3 | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 7c46a2cc-4fe3-4399-8833-61b016730b71 | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2024-05-08T06:32:00.483952 | ... | https://www.opendata-hro.de/dataset/7afdfa7a-7... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Bildung,... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'freizeit', 'id': '73a5f345-... | [] | [] | [{'metric': 'F1A', 'score': '2', 'target_uri':... | 200 |
| 4 | Hanse- und Universitätsstadt Rostock – Hauptamt | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 4d25536c-7cc4-4761-8504-0ba8ca320fa1 | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2024-05-08T06:32:18.276436 | ... | https://www.opendata-hro.de/dataset/aceccfc0-d... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Regierun... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'behörde', 'id': 'e15183d8-d... | [] | [] | [{'metric': 'F1A', 'score': '2', 'target_uri':... | 200 |
5 rows × 31 columns
df2 = df2[df2["assessment_status_code"] == 200]
df2 = df2.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
df2["FAIR_assessment"] = df2["FAIR_assessment"].map(lambda x: build_assessment_json(x))
df2
| author | author_email | creator_user_id | id | isopen | license_id | license_title | maintainer | maintainer_email | metadata_created | ... | type | url | version | extras | groups | resources | tags | relationships_as_subject | relationships_as_object | FAIR_assessment | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Hanse- und Universitätsstadt Rostock – Hauptamt | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 05be0aa5-76a2-4850-b37f-69e623565fbe | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2023-01-06T06:14:11.920576 | ... | dataset | https://www.opendata-hro.de/dataset/e7c10dd4-d... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Regierun... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'politik', 'id': 'a8e6e4aa-6... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 1 | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 44fa7d61-7924-4740-95ec-b4a6821139fe | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2024-05-08T06:31:53.940777 | ... | dataset | https://www.opendata-hro.de/dataset/c276228b-9... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Regierun... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'bauleitplanung', 'id': '861... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 2 | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 6219e4c3-e819-40f0-86cd-543ff829ad6c | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2024-05-08T06:31:46.141017 | ... | dataset | https://www.opendata-hro.de/dataset/655f46c2-1... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Regierun... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'bauleitplanung', 'id': '861... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 3 | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 7c46a2cc-4fe3-4399-8833-61b016730b71 | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2024-05-08T06:32:00.483952 | ... | dataset | https://www.opendata-hro.de/dataset/7afdfa7a-7... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Bildung,... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'freizeit', 'id': '73a5f345-... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 4 | Hanse- und Universitätsstadt Rostock – Hauptamt | geodienste@rostock.de | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 4d25536c-7cc4-4761-8504-0ba8ca320fa1 | False | NaN | NaN | Hanse- und Universitätsstadt Rostock – Kataste... | geodienste@rostock.de | 2024-05-08T06:32:18.276436 | ... | dataset | https://www.opendata-hro.de/dataset/aceccfc0-d... | NaN | [{'key': 'access_rights', 'value': 'http://pub... | [{'description': '', 'display_name': 'Regierun... | [{'access_url': 'https://geo.sv.rostock.de/dow... | [{'display_name': 'behörde', 'id': 'e15183d8-d... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9069 | NaN | NaN | f512f73d-bed2-439d-8f6f-3fbc6048b24e | bed1ad07-1963-4175-bb73-0af79479eede | False | NaN | NaN | NaN | NaN | 2021-06-02T11:24:14.680308 | ... | dataset | https://www.berlin.de/lageso/gesundheit/infekt... | NaN | [{'key': 'conforms_to', 'value': '["http://dca... | [{'description': '', 'display_name': 'Gesundhe... | [{'access_url': 'https://www.berlin.de/lageso/... | [{'display_name': 'covid-19_erkrankungen', 'id... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 9070 | NaN | NaN | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 80c44751-ed80-4433-99f5-4baf108e1155 | False | NaN | NaN | NaN | NaN | 2021-06-02T11:21:15.752075 | ... | dataset | https://www.berlin.de/lageso/gesundheit/infekt... | NaN | [{'key': 'conforms_to', 'value': '["http://dca... | [{'description': '', 'display_name': 'Gesundhe... | [{'access_url': 'https://www.berlin.de/lageso/... | [{'display_name': 'covid-19_erkrankungen', 'id... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 9071 | NaN | NaN | f512f73d-bed2-439d-8f6f-3fbc6048b24e | daaf2c24-652d-474b-a9ae-aad1ec9a3a6c | False | NaN | NaN | NaN | NaN | 2024-01-05T08:52:48.735472 | ... | dataset | https://www.berlin.de/sen/arbeit/weiterbildung... | NaN | [{'key': 'conforms_to', 'value': '["http://dca... | [{'description': '', 'display_name': 'Wirtscha... | [{'access_url': 'https://www.berlin.de/sen/arb... | [{'display_name': 'bildungszeit', 'id': '3e3a2... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 9072 | NaN | NaN | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 092f7b48-66bd-465d-ab94-49c237470fdf | False | NaN | NaN | NaN | NaN | 2018-07-27T08:08:07.793153 | ... | dataset | https://www.berlin.de/lageso/gesundheit/gesund... | NaN | [{'key': 'conforms_to', 'value': '["http://dca... | [{'description': '', 'display_name': 'Gesundhe... | [{'access_url': 'https://www.berlin.de/lageso/... | [{'display_name': 'badegewässer', 'id': '4bc11... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 9073 | NaN | NaN | f512f73d-bed2-439d-8f6f-3fbc6048b24e | 2444f126-3d4a-4421-8d40-a8ddc4b73a7a | False | NaN | NaN | NaN | NaN | 2024-02-02T07:46:07.127646 | ... | dataset | https://www.berlin.de/osz/osz-karte/ | NaN | [{'key': 'conforms_to', 'value': '["http://dca... | [{'description': '', 'display_name': 'Bildung,... | [{'access_url': 'https://www.berlin.de/osz/osz... | [{'display_name': 'berufsschule', 'id': '9f91c... | [] | [] | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
9074 rows × 30 columns
metrics_dicts = df2['FAIR_assessment'].apply(convert_metrics_to_dict)
# Create a new DataFrame from the list of dictionaries
metrics_df2 = pd.DataFrame(metrics_dicts.tolist())
df_combined2 = pd.concat([df2, metrics_df2], axis=1, ignore_index=True)
columns = df_combined2.columns
new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']
# Renaming the last 12 columns
df_combined2.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)
for col in new_column_names:
df_combined2[col] = pd.to_numeric(df_combined2[col], errors='coerce')
df_combined2['Score'] = df_combined2[new_column_names].sum(axis=1)
print_summary_stats(df_combined2)
Mean: 6.320255675556536 Median: 5.0 Max: 16 Min: 0
df3 = pd.read_csv(r"./ogdmetadatendatagvat_assessed_swiss2024-06-12.csv", index_col = [0])
df3 = df3[df3["assessment_status_code"] == 200]
df3 = df3.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
df3["FAIR_assessment"] = df3["FAIR_assessment"].map(lambda x: build_assessment_json(x))
df3
| Unnamed: 0 | owner_org | maintainer | issued | title_for_slug | qualified_relations | private | maintainer_email | num_tags | contact_points | ... | documentation | modified | url | see_alsos | revision_id | identifier | spatial | coverage | extras | FAIR_assessment | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | b3d5c5d8-f736-459e-b23b-fe0ea6196cf2 | Sara Schärrer | 2023-03-02T00:00:00 | Tierseuchenmeldungen in der Schweiz | [] | False | sara.schaerrer@blv.admin.ch | 9 | [{'name': 'Kompetenzzentrum Daten', 'email': '... | ... | [] | NaN | https://infosm.blv.admin.ch/ | [] | c8793a79-b0ff-4e59-98bc-0dec70e90e88 | tierseuchen@bundesamt-fur-lebensmittelsicherhe... | NaN | NaN | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 1 | 25 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2023-08-30T00:00:00 | klimaanalyse-windgeschwindigkeit-nacht-zukunft... | [] | False | NaN | 0 | [{'email': 'afu@bd.so.ch', 'name': 'afu@bd.so.... | ... | NaN | 2023-08-30T00:00:00 | https://data.geo.so.ch?filter=ch.so.afu.klimaa... | [] | 5d7ec063-fd29-4322-9c33-04bc607e72c1 | f5312f7d-dba5-4ca3-8419-de3fea19d6c7@kanton_so... | Kanton Solothurn (SO) | NaN | [{'key': 'harvest_object_id', 'value': '230dfa... | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 2 | 26 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2024-06-06T00:00:00 | gebaudeflachen-mit-adressen-amtliche-vermessung2 | [] | False | NaN | 0 | [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... | ... | NaN | 2024-06-06T00:00:00 | https://data.geo.so.ch?filter=ch.so.agi.av.geb... | [] | cd1c1574-bab5-46a7-a55b-13d026cc857c | 1e984455-1980-4ba0-b87c-633b0971d99d@kanton_so... | Kanton Solothurn (SO) | NaN | [{'key': 'harvest_object_id', 'value': '10425c... | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 3 | 27 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2024-01-31T00:00:00 | kantonsstrassen1 | [] | False | NaN | 0 | [{'email': 'avt@bd.so.ch', 'name': 'avt@bd.so.... | ... | ['https://geo.so.ch/map?l=ch.so.avt.kantonsstr... | 2024-01-31T00:00:00 | https://data.geo.so.ch?filter=ch.so.avt.kanton... | [] | 4491b576-5897-4ea9-9f89-fcc121cec591 | 9209c1e4-6adf-452b-9870-235a6dbe01bb@kanton_so... | Kanton Solothurn (SO) | NaN | [{'key': 'harvest_object_id', 'value': 'd4e9a6... | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 4 | 29 | 51ffc9d5-bf18-4bb4-9b26-ad9b45a60e14 | Stadt Winterthur, Stadtentwicklung der Stadt W... | 2022-09-20T17:10:34+01:00 | beschaftigtendichte-in-der-stadt-winterthur-ab... | [] | False | statistik@win.ch | 4 | [{'email': 'statistik@win.ch', 'name': 'Stadt ... | ... | [] | 2024-06-04T16:54:18+01:00 | https://stadt.winterthur.ch/themen/die-stadt/w... | [{'dataset_identifier': '1521@stadt-winterthur... | 449f19b9-7b35-4d6e-beeb-2f3492da35e6 | 1621@stadt-winterthur | NaN | NaN | [{'value': '1621@stadt-winterthur', 'key': 'gu... | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10253 | 11886 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2023-02-08T00:00:00 | gebaude-digitales-oberflachenmodell-lidar-2018 | NaN | False | NaN | 0 | [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... | ... | NaN | 2023-02-08T00:00:00 | https://data.geo.so.ch?filter=ch.bl.agi.lidar_... | {} | 30c3a281-a904-47d6-ae6c-fc0429ff41b3 | bfdbe5cc-f512-4ef3-bafa-8aefbe04c352@kanton_so... | Kanton Solothurn (SO) | NaN | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 10254 | 11887 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2023-01-18T00:00:00 | administrative-einteilung-av-und-grundbuch | NaN | False | NaN | 0 | [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... | ... | NaN | 2023-01-18T00:00:00 | https://data.geo.so.ch?filter=ch.so.agi.av.adm... | {} | 1cf92303-d3d8-4cd9-94a5-8532dce645c3 | d596090e-2f5f-4c42-885c-477b72dc2ed7@kanton_so... | Kanton Solothurn (SO) | NaN | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 10255 | 11888 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2023-03-01T00:00:00 | strassenlarm | NaN | False | NaN | 0 | [{'email': 'avt@bd.so.ch', 'name': 'avt@bd.so.... | ... | NaN | 2023-03-01T00:00:00 | https://data.geo.so.ch?filter=ch.so.avt.strass... | {} | e5feee8d-48cc-4a29-bf79-fd60eb0a9c16 | debd0f63-9127-4585-bca6-1f6832872472@kanton_so... | Kanton Solothurn (SO) | NaN | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 10256 | 11889 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2023-02-08T00:00:00 | orthofoto-2012-rgb | NaN | False | NaN | 0 | [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... | ... | NaN | 2023-02-08T00:00:00 | https://data.geo.so.ch?filter=ch.so.agi.orthof... | {} | ab641c3d-478e-4cfe-8ad0-eb69f616e6a2 | cb8d5956-0b22-4ec0-be97-ffbdc2fcbd1d@kanton_so... | Kanton Solothurn (SO) | NaN | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
| 10257 | 11890 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2023-03-01T00:00:00 | waldwanderung | NaN | False | NaN | 0 | [{'email': 'awjf@vd.so.ch', 'name': 'awjf@vd.s... | ... | NaN | 2023-03-01T00:00:00 | https://data.geo.so.ch?filter=ch.so.awjf.waldw... | {} | 8a532052-8def-47d9-9439-cec72d386c24 | 5e9abe1f-eeaa-4771-8830-82f192e365bd@kanton_so... | Kanton Solothurn (SO) | NaN | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... |
10258 rows × 51 columns
metrics_dicts = df3['FAIR_assessment'].apply(convert_metrics_to_dict)
# Create a new DataFrame from the list of dictionaries
metrics_df3 = pd.DataFrame(metrics_dicts.tolist())
df_combined3 = pd.concat([df3, metrics_df3], axis=1, ignore_index=True)
columns = df_combined3.columns
new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']
# Renaming the last 12 columns
df_combined3.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)
for col in new_column_names:
df_combined3[col] = pd.to_numeric(df_combined3[col], errors='coerce')
df_combined3['Score'] = df_combined3[new_column_names].sum(axis=1)
Mean: 6.320255675556536 Median: 5.0 Max: 16 Min: 0
df4 = pd.read_csv(r"./ogdmetadatendatagvat_assessed_irish2024-06-12.csv", index_col = [0])
len(df4)
15000
df4 = df4[df4["assessment_status_code"] == 200]
df4 = df4.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
# When running the same functions we encountered an issue with JSON formatting
# So we slightly modified the code such that it handles the error
def build_assessment_json2(assessment):
regex_result = re.findall(r"'metric': '.*?', 'score': '.*?'", assessment)
assessment_list = []
for result in regex_result:
result = result.replace("\'", "\"") # Replace single quotes with double quotes
json_string = "{" + result + "}" # Construct the JSON string
try:
print(f"Parsing JSON string: {json_string}") # Debug print statement
assessment_dict = json.loads(json_string) # Parse JSON string
assessment_list.append(assessment_dict) # Append to list
except json.JSONDecodeError as e:
print(f"Error parsing JSON string: {json_string}")
print(f"JSONDecodeError: {e}")
return assessment_list
df4["FAIR_assessment"] = df4["FAIR_assessment"].map(lambda x: build_assessment_json2(x))
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "2"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "2"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "2"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "2"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "2"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "2"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "1"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "1"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "1"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "1"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "1"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "1"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "1"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "1"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
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Parsing JSON string: {"metric": "I2", "score": "1"}
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Parsing JSON string: {"metric": "R1.3", "score": "1"}
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Parsing JSON string: {"metric": "F2A", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "R1.3", "score": "2"}
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Parsing JSON string: {"metric": "F2A", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "R1.3", "score": "2"}
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Parsing JSON string: {"metric": "F2A", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "R1.3", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "F2A", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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Parsing JSON string: {"metric": "I1", "score": "1"}
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JSONDecodeError: Expecting value: line 1 column 28 (char 27)
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Parsing JSON string: {"metric": "F1A", "score": "2"}
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Parsing JSON string: {"metric": "I3", "score": "0"}
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Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
IOPub data rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_data_rate_limit`. Current values: NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec) NotebookApp.rate_limit_window=3.0 (secs)
metrics_dicts = df4['FAIR_assessment'].apply(convert_metrics_to_dict)
# Create a new DataFrame from the list of dictionaries
metrics_df4 = pd.DataFrame(metrics_dicts.tolist())
df_combined4 = pd.concat([df4, metrics_df4], axis=1, ignore_index=True)
columns = df_combined4.columns
new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']
# Renaming the last 12 columns
df_combined4.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)
for col in new_column_names:
df_combined4[col] = pd.to_numeric(df_combined4[col], errors='coerce')
df_combined4['Score'] = df_combined4[new_column_names].sum(axis=1)
df = df[df["assessment_status_code"] == 200]
df = df.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
df["FAIR_assessment"] = df["FAIR_assessment"].map(lambda x: build_assessment_json(x))
metrics_dicts = df['FAIR_assessment'].apply(convert_metrics_to_dict)
# Create a new DataFrame from the list of dictionaries
metrics_df = pd.DataFrame(metrics_dicts.tolist())
df_combined = pd.concat([df, metrics_df], axis=1, ignore_index=True)
columns = df_combined.columns
new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']
# Renaming the last 12 columns
df_combined.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)
for col in new_column_names:
df_combined[col] = pd.to_numeric(df_combined[col], errors='coerce')
df_combined['Score'] = df_combined[new_column_names].sum(axis=1)
print_summary_stats(df_combined)
print_summary_stats(df_combined2)
print_summary_stats(df_combined3)
print_summary_stats(df_combined4)
Mean: 14.021220909669662 Median: 15.0 Max: 19 Min: 0 Mean: 6.320255675556536 Median: 5.0 Max: 16 Min: 0 Mean: 9.01550009748489 Median: 9.0 Max: 24 Min: 0 Mean: 5.2843391902215435 Median: 4.0 Max: 22.0 Min: 0.0
import os
os.getcwd()
'C:\\Users\\MolnarM\\Desktop\\Notebooks\\Uni'
df.to_excel('austria_df.xlsx')
df2.to_excel('germany_df.xlsx')
df3.to_excel('switzerland_df.xlsx')
df4.to_excel('ireland_df.xlsx')
df_combined4.columns
Index([ 0, 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41,
42, 43, 44, 45, 46, 47,
48, 49, 50, 51, 52, 53,
54, 55, 56, 'F1A', 'F1B', 'F2A',
'F2B', 'A1.1', 'A1.2', 'I1', 'I2', 'I3',
'R1.1', 'R1.2', 'R1.3', 'Score', 'Country'],
dtype='object')
df_combined4
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | NaN | NaN | NaN | NaN | NaN | 0863c6bf-d7d8-485b-94b7-d50a11d5796d | Continuous | f2d3c4fc-0ea7-4959-98c5-598241a5ea51 | True | ... | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
| 1 | NaN | NaN | NaN | opendata@tailte.ie | NaN | NaN | f98da442-3307-463d-b676-a327e93755ec | Continuous | 6eccd685-3dec-481b-9b9f-3f957ba9ddcd | True | ... | 2.0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 12.0 | Ireland |
| 2 | NaN | NaN | NaN | opendata@met.ie | Kieran Harper | NaN | f1eafd65-adc1-4721-aefc-fa9b6c9b3a0c | Continuous | 0a8b1aa8-f49b-411e-a120-b0cd0297c2a1 | True | ... | 2.0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 12.0 | Ireland |
| 3 | NaN | NaN | swagger 2.0, Akoma Ntoso | open.data@oireachtas.ie | Open Data Liaison Officer | (01) 6183000 | ef08264c-676d-48c5-aaad-6ce8aa18d521 | Continuously updated | d9ddc7ee-d497-41bb-924f-3557cfa12037 | True | ... | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
| 4 | NaN | NaN | NaN | NaN | NaN | NaN | ef08264c-676d-48c5-aaad-6ce8aa18d521 | Weekly | 18020dbe-8565-47d0-8632-a8fc4dc540a0 | True | ... | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 13085 | NaN | NaN | NaN | NaN | NaN | NaN | 4bb8ac75-49de-49b1-8568-ea7ba09886f8 | NaN | bc4dc2d9-1c0b-421a-98e0-b4b555402168 | True | ... | 2.0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 12.0 | Ireland |
| 13086 | NaN | NaN | NaN | NaN | NaN | NaN | 4bb8ac75-49de-49b1-8568-ea7ba09886f8 | NaN | 7a1b5db7-ae22-4169-8aa8-19814f32514b | True | ... | 2.0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 12.0 | Ireland |
| 13087 | Environmental Protection Agency | AIRQD@epa.ie | The INSPIRE Directive or INSPIRE lays down a g... | NaN | NaN | NaN | ef08264c-676d-48c5-aaad-6ce8aa18d521 | Other | 2089f142-1f00-4526-9728-ea81fcecc8f9 | True | ... | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
| 13088 | Environmental Protection Agency | AIRQD@epa.ie | The INSPIRE Directive or INSPIRE lays down a g... | NaN | NaN | NaN | ef08264c-676d-48c5-aaad-6ce8aa18d521 | Other | 2803f489-ce32-4543-98ff-d14084b5f1bb | True | ... | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
| 13089 | Environmental Protection Agency | AIRQD@epa.ie | The INSPIRE Directive or INSPIRE lays down a g... | NaN | NaN | NaN | ef08264c-676d-48c5-aaad-6ce8aa18d521 | Other | f7cf61c6-c708-4529-bcce-dc331a630c1c | True | ... | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
13090 rows × 71 columns
# Adding a country column so that it can be used for filtering/grouping
df_combined['Country'] = 'Austria'
#df_combined.head()
df_combined2['Country'] = 'Germany'
df_combined3['Country'] = 'Switzerland'
df_combined4['Country'] = 'Ireland'
# The metadata columns are slightly different for each country, only the metrics are identical
# Creating a dataframe that only contains the scores for the metrics (and score and country)
subset_df1 = df_combined.iloc[:, -14:]
subset_df2 = df_combined2.iloc[:, -14:]
subset_df3 = df_combined3.iloc[:, -14:]
subset_df4 = df_combined4.iloc[:, -14:]
# Concatenate the subsets into one big DataFrame
combined_df = pd.concat([subset_df1, subset_df2, subset_df3, subset_df4], axis=0)
# Reset the index to ensure it is unique
combined_df.reset_index(drop=True, inplace=True)
combined_df
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria |
| 1 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria |
| 2 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria |
| 3 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria |
| 4 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 46554 | 2 | 0 | 1 | 2.0 | 2.0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 12.0 | Ireland |
| 46555 | 2 | 0 | 1 | 2.0 | 2.0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 12.0 | Ireland |
| 46556 | 2 | 0 | 0 | 0.0 | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
| 46557 | 2 | 0 | 0 | 0.0 | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
| 46558 | 2 | 0 | 0 | 0.0 | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.0 | Ireland |
46559 rows × 14 columns
# Simple horizontal barplot for average Scores per country
eval1_df = combined_df.groupby("Country").agg({"Score": "mean"})
eval1_df["counts"] = combined_df["Country"].value_counts()
eval1_df = eval1_df.sort_values("counts").reset_index()
plot = plt.figure()
ax = plot.add_axes([0,0,1,1])
fields = eval1_df["Country"]
avg_scores = eval1_df["Score"]
ax.barh(fields, avg_scores, color=counts_color)
plt.show()
# Creating subgroups of the metrics corresponding to the FAIR Principles
combined_df['Fair'] = combined_df.iloc[:, 0:4].sum(axis=1)
combined_df['Accessible'] = combined_df.iloc[:, 4:6].sum(axis=1)
combined_df['Interoperable'] = combined_df.iloc[:, 6:9].sum(axis=1)
combined_df['Reusable'] = combined_df.iloc[:, 9:12].sum(axis=1)
combined_df.head()
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Country | Fair | Accessible | Interoperable | Reusable | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria | 4.0 | 4.0 | 2 | 5 |
| 1 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria | 4.0 | 4.0 | 2 | 5 |
| 2 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria | 4.0 | 4.0 | 2 | 5 |
| 3 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria | 4.0 | 4.0 | 2 | 5 |
| 4 | 2 | 0 | 1 | 1.0 | 2.0 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 15.0 | Austria | 4.0 | 4.0 | 2 | 5 |
avg_1_4 = combined_df['Fair'].mean()
avg_5_6 = combined_df['Accessible'].mean()
avg_7_9 = combined_df['Interoperable'].mean()
avg_10_12 = combined_df['Reusable'].mean()
# List of perfect scores
perfect_scores = [8, 4, 6, 6]
# List of actual average scores
actual_scores = [avg_1_4, avg_5_6, avg_7_9, avg_10_12]
# Column names
columns = ['Fair', 'Accessible', 'Interoperable', 'Reusable']
# Indices for the bars
indices = np.arange(len(perfect_scores))
width = 0.4 # width of the bars
# Plotting
plt.figure(figsize=(10, 6))
plt.bar(indices, perfect_scores, width=width, color='b', alpha=0.5, label='Perfect Score')
plt.bar(indices, actual_scores, width=0.5*width, color='orange', label='Average Score')
# Customize the plot
plt.xticks(indices, columns)
plt.ylabel('Scores')
plt.title('Average Scores vs Perfect Scores')
plt.legend()
# Show the plot
plt.show()
country_colors = {
'Austria': 'orange',
'Germany': 'green',
'Switzerland': 'red',
'Ireland': 'purple'
}
aspects = ['Findable', 'Interoperable', 'Accessible', 'Reusable']
# Plotting
fig, ax = plt.subplots(figsize=(12, 8))
width = 0.15 # Width of the individual country bars
perfect_width = width * len(country_colors) # Width of the perfect score bars
indices = np.arange(len(max_values))
# Plot bars for perfect scores
ax.bar(indices, max_values, width=perfect_width, color='blue', alpha=0.3, label='Perfect Score')
# Plot bars for each country
for i, (country, color) in enumerate(country_colors.items()):
actual_scores = grouped.loc[country]
positions = indices + (i - len(country_colors)/2 + 0.5) * width # Center the smaller bars on the perfect score bar
ax.bar(positions, actual_scores, width=0.5*width, color=color, label=country)
# Customize the plot
ax.set_xticks(indices)
ax.set_xticklabels(aspects)
ax.set_ylabel('Scores')
ax.set_title('Average Scores vs Perfect Scores by Country and Aspect')
ax.legend()
plt.show()
display(combined_df.groupby('Country').mean())
display(combined_df.groupby('Country').median())
display(combined_df.groupby('Country').min())
display(combined_df.groupby('Country').max())
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Fair | Accessible | Interoperable | Reusable | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||
| Austria | 1.989531 | 0.007003 | 0.994836 | 0.994766 | 1.989531 | 1.529603 | 0.994836 | 0.994766 | 0.012450 | 1.529603 | 1.989531 | 0.994766 | 14.021221 | 3.986136 | 3.519134 | 2.002051 | 4.513900 |
| Germany | 1.330395 | 0.000000 | 0.690544 | 0.720079 | 1.330395 | 0.059070 | 0.690544 | 0.720079 | 0.000000 | 0.000000 | 0.059070 | 0.720079 | 6.320256 | 2.741018 | 1.389464 | 1.410624 | 0.779149 |
| Ireland | 1.978915 | 0.005424 | 0.192208 | 0.295606 | 1.990221 | 0.009473 | 0.191444 | 0.296104 | 0.007181 | 0.009778 | 0.011001 | 0.297250 | 5.284339 | 2.472040 | 1.999542 | 0.494729 | 0.318029 |
| Switzerland | 1.978748 | 0.166212 | 0.662312 | 0.823357 | 1.986157 | 0.280562 | 0.662312 | 0.823357 | 0.329889 | 0.280367 | 0.198674 | 0.823552 | 9.015500 | 3.630630 | 2.266719 | 1.815559 | 1.302593 |
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Fair | Accessible | Interoperable | Reusable | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||
| Austria | 2.0 | 0.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 0.0 | 2.0 | 2.0 | 1.0 | 15.0 | 4.0 | 4.0 | 2.0 | 5.0 |
| Germany | 2.0 | 0.0 | 1.0 | 1.0 | 2.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 5.0 | 2.0 | 2.0 | 2.0 | 1.0 |
| Ireland | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 |
| Switzerland | 2.0 | 0.0 | 1.0 | 1.0 | 2.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 9.0 | 4.0 | 2.0 | 2.0 | 1.0 |
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Fair | Accessible | Interoperable | Reusable | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||
| Austria | 0 | 0 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| Germany | 0 | 0 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| Ireland | 0 | 0 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| Switzerland | 0 | 0 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Fair | Accessible | Interoperable | Reusable | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||
| Austria | 2 | 2 | 2 | 1.0 | 2.0 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 19.0 | 6.0 | 4.0 | 5 | 5 |
| Germany | 2 | 0 | 1 | 2.0 | 2.0 | 2 | 1 | 2 | 0 | 0 | 2 | 2 | 16.0 | 5.0 | 4.0 | 3 | 4 |
| Ireland | 2 | 2 | 1 | 2.0 | 2.0 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 22.0 | 7.0 | 4.0 | 5 | 6 |
| Switzerland | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | 8.0 | 4.0 | 6 | 6 |
len(combined_df[combined_df['Score']==24])
280
# All perfect scores are Swiss datasets - Bioinformatcs theme!
combined_df[(combined_df['Score']==24)&(combined_df['Country']!='Switzerland')]
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Country | Fair | Accessible | Interoperable | Reusable |
|---|
combined_df[combined_df['Score']==24]
| F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Country | Fair | Accessible | Interoperable | Reusable | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 23779 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| 23780 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| 23782 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| 23783 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| 23784 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 24288 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| 24312 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| 24330 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| 24338 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
| 24340 | 2 | 2 | 2 | 2.0 | 2.0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 24.0 | Switzerland | 8.0 | 4.0 | 6 | 6 |
280 rows × 18 columns
dfq = combined_df.groupby('Country').mean()
import matplotlib.pyplot as plt
import seaborn as sns
# Set the plotting style
sns.set(style="whitegrid")
# Create a bar plot for the average FAIR scores
dfq.plot(kind='bar', figsize=(14, 8))
plt.title('Average FAIR Scores by Country')
plt.xlabel('FAIR Metrics')
plt.ylabel('Average Score')
plt.legend(title='Country')
plt.xticks(rotation=45)
plt.show()
All metric averages correspond to the overall order - no anomalies or significant outliers.
melted_scores = pd.melt(combined_df, id_vars='Country', var_name='FAIR Metric', value_name='Score')
C:\Users\local_MolnarM\Temp\ipykernel_93292\848975422.py:1: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resulting Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name. melted_scores = pd.melt(combined_df, id_vars='Country', var_name='FAIR Metric', value_name='Score')
plt.figure(figsize=(14, 8))
sns.boxplot(x='FAIR Metric', y='Score', hue='Country', data=melted_scores)
plt.title('Distribution of FAIR Scores by Country')
plt.xlabel('FAIR Metrics')
plt.ylabel('Score')
plt.xticks(rotation=45)
plt.legend(title='Country')
plt.show()
Austrian and Irish datasets achieve very similar scores (apparently the majority of the datasets are published by a handful of organizations that use consistently the same standards when publishing) - little variation - that's why the boxplots look like this.
But it yields quite meaningless results as all the metadata was modified in 2022/23 so we leave it out from the visualizations.
# Convert the date columns to datetime
df['metadata_created'] = pd.to_datetime(df['metadata_modified'])
df['metadata_modified'] = pd.to_datetime(df['metadata_modified'])
df2['metadata_created'] = pd.to_datetime(df2['metadata_created'])
df2['metadata_modified'] = pd.to_datetime(df2['metadata_modified'])
df3['metadata_created'] = pd.to_datetime(df3['metadata_created'])
df3['metadata_modified'] = pd.to_datetime(df3['metadata_modified'])
df4['metadata_created'] = pd.to_datetime(df4['metadata_created'])
df4['metadata_modified'] = pd.to_datetime(df4['metadata_modified'])
# Extract the year from the datetime columns
df['year_created'] = df['metadata_created'].dt.year
df['year_modified'] = df['metadata_modified'].dt.year
df2['year_created'] = df2['metadata_created'].dt.year
df2['year_modified'] = df2['metadata_modified'].dt.year
df3['year_created'] = df3['metadata_created'].dt.year
df3['year_modified'] = df3['metadata_modified'].dt.year
df4['year_created'] = df4['metadata_created'].dt.year
df4['year_modified'] = df4['metadata_modified'].dt.year
# Adding back the scores to the original dataframes so we have the column names
df = pd.concat([df,df_combined.iloc[:,-14:]], axis = 1)
df2 = pd.concat([df2,df_combined2.iloc[:,-14:]], axis = 1)
df3 = pd.concat([df3,df_combined3.iloc[:,-14:]], axis = 1)
df4 = pd.concat([df4,df_combined4.iloc[:,-14:]], axis = 1)
# Define the FAIR metrics columns
fair_metrics = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1', 'A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']
# Aggregate average FAIR scores by year for each country
austria_trend = df.groupby('year_created')[fair_metrics].mean().reset_index()
germany_trend = df2.groupby('year_created')[fair_metrics].mean().reset_index()
switzerland_trend = df3.groupby('year_created')[fair_metrics].mean().reset_index()
ireland_trend = df4.groupby('year_created')[fair_metrics].mean().reset_index()
import matplotlib.pyplot as plt
# Function to plot trends
def plot_trends(country_trend, country_name):
plt.figure(figsize=(14, 8))
for metric in fair_metrics:
plt.plot(country_trend['year_created'], country_trend[metric], label=metric)
plt.title(f'Trend of FAIR Scores Over Time in {country_name}')
plt.xlabel('Year')
plt.ylabel('Average FAIR Score')
plt.legend(title='FAIR Metric', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True)
plt.show()
# Plot trends for each country
#plot_trends(austria_trend, 'Austria')
plot_trends(germany_trend, 'Germany')
plot_trends(switzerland_trend, 'Switzerland')
plot_trends(ireland_trend, 'Ireland')
Making the trendlines a bit smoother.
from scipy.interpolate import make_interp_spline
# Function to plot trends with smoother lines
def plot_trends(country_trend, country_name):
plt.figure(figsize=(14, 8))
x = country_trend['year_created']
for metric in fair_metrics:
y = country_trend[metric]
X_Y_Spline = make_interp_spline(x, y)
# Generate more x values to create a smooth curve
X_ = np.linspace(min(x), max(x), 500)
Y_ = X_Y_Spline(X_)
plt.plot(X_, Y_, label=metric)
plt.title(f'Trend of FAIR Scores Over Time in {country_name}')
plt.xlabel('Year')
plt.ylabel('Average FAIR Score')
plt.legend(title='FAIR Metric', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True)
plt.show()
# Plot trends for each country
# plot_trends(austria_trend, 'Austria')
plot_trends(germany_trend, 'Germany')
plot_trends(switzerland_trend, 'Switzerland')
plot_trends(ireland_trend, 'Ireland')
# Function to calculate average and plot trend
def plot_average_trend(countries):
plt.figure(figsize=(14, 8))
years = countries[0]['year_created']
metrics = fair_metrics
for idx, metric in enumerate(metrics):
# Calculate average for each metric across all countries
avg_scores = np.mean([country[metric] for country in countries], axis=0)
# Smoothing the line
X_Y_Spline = make_interp_spline(years, avg_scores)
X_ = np.linspace(min(years), max(years), 500)
Y_ = X_Y_Spline(X_)
# Plotting
plt.plot(X_, Y_, label=metric)
plt.title('Average Trend of FAIR Scores Over Time Across Countries')
plt.xlabel('Year')
plt.ylabel('Average FAIR Score')
plt.legend(title='FAIR Metric')
plt.grid(True)
plt.show()
# List of country data
countries = [germany_trend[1:], switzerland_trend[4:], ireland_trend[1:]]
# Plot the average trends
plot_average_trend(countries)
No visible improvements over the last years for any country - if anything, there's a small downwards trend from 2022.
def plot_heatmap(country_trend, country_name):
plt.figure(figsize=(14, 8))
sns.heatmap(country_trend.set_index('year_created')[fair_metrics].T, annot=True, cmap="YlGnBu")
plt.title(f'Heatmap of FAIR Scores Over Time in {country_name}')
plt.xlabel('Year')
plt.ylabel('FAIR Metrics')
plt.show()
# Plot heatmaps for each country
#plot_heatmap(austria_trend, 'Austria')
plot_heatmap(germany_trend, 'Germany')
plot_heatmap(switzerland_trend, 'Switzerland')
plot_heatmap(ireland_trend, 'Ireland')
The heatmaps confirm the findings of the trendlines.
# Aggregate average FAIR scores for each country
mean_scores = {
'Austria': df[fair_metrics].mean(),
'Germany': df2[fair_metrics].mean(),
'Switzerland': df3[fair_metrics].mean(),
'Ireland': df4[fair_metrics].mean()
}
mean_scores_df = pd.DataFrame(mean_scores).T
# Create a stacked bar chart
mean_scores_df.plot(kind='bar', stacked=True, figsize=(14, 8))
plt.title('Average FAIR Scores by Country')
plt.xlabel('Country')
plt.ylabel('Average Score')
plt.legend(title='FAIR Metrics', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
fair_metrics
['F1A', 'F1B', 'F2A', 'F2B', 'A1.1', 'A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']
from math import pi
# Function to create radar chart
def plot_radar_chart(data, country_name):
# Number of variables we're plotting
categories = fair_metrics
N = len(categories)
# What will be the angle of each axis in the plot? (we divide the plot / number of variables)
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]
# Initialise the radar plot
ax = plt.subplot(111, polar=True)
# If you want the first axis to be on top:
ax.set_theta_offset(pi / 2)
ax.set_theta_direction(-1)
# Draw one axe per variable + add labels
plt.xticks(angles[:-1], categories)
ax.set_yticklabels([])
# Draw ylabels
ax.set_rlabel_position(0)
plt.yticks([0.2, 0.4, 0.6, 0.8, 1], color="grey", size=7)
plt.ylim(0, max_score)
# Ind1
values = data.values.flatten().tolist()
values += values[:1]
ax.plot(angles, values, linewidth=1, linestyle='solid', label=country_name)
ax.fill(angles, values, alpha=0.1)
# Add a title
plt.title(f'Radar Chart of the scores per Principles', size=20, color='black', y=1.1)
# Prepare data for radar chart
mean_scores_radar = {
'Austria': df[fair_metrics].mean(),
'Germany': df2[fair_metrics].mean(),
'Switzerland': df3[fair_metrics].mean(),
'Ireland': df4[fair_metrics].mean()
}
max_score = 2
# Plot radar charts for each country
plt.figure(figsize=(10, 10))
plot_radar_chart(mean_scores_radar['Austria'], 'Austria')
plot_radar_chart(mean_scores_radar['Germany'], 'Germany')
plot_radar_chart(mean_scores_radar['Switzerland'], 'Switzerland')
plot_radar_chart(mean_scores_radar['Ireland'], 'Ireland')
plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
plt.show()
Overall similar "strengths and weaknesses", but the data frm the Austrian portal tends to always have slightly higher scores.
F1B and I3 metrics are (virtually) always 0.
Not designed as no meaningful results came out.
import matplotlib.pyplot as plt
# Function to plot pie charts for categorical data
def plot_pie_chart(data, column, country_name):
plt.figure(figsize=(8, 8))
data[column].value_counts().plot.pie(autopct='%1.1f%%', startangle=140, colors=sns.color_palette("pastel"))
plt.title(f'Distribution of {column} in {country_name}')
plt.ylabel('')
plt.show()
# Plot pie charts for each country and a specific column, e.g., 'license_id'
plot_pie_chart(df, 'license', 'Austria')
#plot_pie_chart(df2, 'license_id', 'Germany')
#plot_pie_chart(df3, 'license_id', 'Switzerland')
plot_pie_chart(df4, 'license_title', 'Ireland')
plot_pie_chart(df, 'schema_language', 'Austria')
#plot_pie_chart(df2, 'language', 'Germany')
plot_pie_chart(df3, 'language', 'Switzerland')
plot_pie_chart(df4, 'language', 'Ireland')
len(df4)
13090
Interestingly the overwhelming majority of Austrian and Irish datasets have almost identical scores, while the German and Swiss datasets take a much wider range.
# Create the boxplot
plt.figure(figsize=(12, 8))
sns.boxplot(x='Country', y='Score', data=combined_df)
plt.title('Boxplot of Scores by Country')
plt.xticks(rotation=90) # Rotate country names if they are long
plt.xlabel('Country')
plt.ylabel('Score')
plt.show()
display(df.publisher.value_counts())
#display(df.maintainer.value_counts())
Offenerhaushalt.at 6965
Nationalparks Austria 3531
Umweltbundesamt GmbH 791
Statistik Austria 394
Land Vorarlberg 379
...
AEP Planung und Beratung GmbH 1
Monopolverwaltung GmbH 1
ASFINAG 1
Stadtgemeinde Trofaiach 1
Gebetsberger ZT GmbH 1
Name: publisher, Length: 65, dtype: int64
display(df2.author.value_counts())
#display(df2.maintainer.value_counts())
Institut für Hygiene und Umwelt 3031 Landesbetrieb Geoinformation und Vermessung 2895 Statistisches Amt für Hamburg und Schleswig-Holstein 2794 Hanse- und Universitätsstadt Rostock – Kataster-, Vermessungs- und Liegenschaftsamt 110 Behörde für Umwelt, Klima, Energie und Agrarwirtschaft (BUKEA) 61 Hanse- und Universitätsstadt Rostock – Hauptamt 51 Hanse- und Universitätsstadt Rostock – Amt für Umwelt- und Klimaschutz 33 Hanse- und Universitätsstadt Rostock – Stadtamt 13 Behörde für Stadtentwicklung und Wohnen (BSW) 10 Hanse- und Universitätsstadt Rostock – Amt für Stadtgrün, Naturschutz und Friedhofswesen 8 Hanse- und Universitätsstadt Rostock – Tiefbauamt 6 Steinbeis-Transferzentrum Geoinformatik 5 Tourismuszentrale Rostock und Warnemünde 5 Hanse- und Universitätsstadt Rostock – Amt für Stadtentwicklung, Stadtplanung und Mobilität 5 Hanse- und Universitätsstadt Rostock – Amt für Kultur, Denkmalpflege und Museen 3 Stadtwerke Rostock AG 3 Landesamt für Innere Verwaltung Mecklenburg-Vorpommern 3 Hanse- und Universitätsstadt Rostock – Stadtforstamt 3 Hanse- und Universitätsstadt Rostock – Brandschutz- und Rettungsamt 2 Hanse- und Universitätsstadt Rostock – Amt für Soziales und Teilhabe 2 Hanse- und Universitätsstadt Rostock – Hafen- und Seemannsamt 2 Rostocker Freizeitzentrum e. V. 2 Hanse- und Universitätsstadt Rostock – Amt für Stadtentwicklung, Stadtplanung und Wirtschaft 2 Statistisches Amt für Hamburg und Schleswig-Holstein, Vertrieb des Statistikamts Nord 2 Hanse- und Universitätsstadt Rostock – Amt für Umweltschutz 1 Bezirksämter 1 Kulturbehörde 1 Staatliches Amt für Landwirtschaft und Umwelt Mittleres Mecklenburg 1 Friedrich Wilhelm Carl Graf von Schmettau 1 Statistisches Amt für Hamburg und Schleswig-Holstein, Webangebote 1 Behörde für Umwelt und Energie (BUE), Amt für Umweltschutz, Geologisches Landesamt 1 Carl Friedrich von Wiebeking 1 Max-Samuel-Haus – Stiftung Begegnungsstätte für jüdische Geschichte und Kultur in Rostock 1 Julius Michael Tarnow 1 Hanse- und Universitätsstadt Rostock – Bauamt 1 OpenStreetMap 1 Eine-Welt-Landesnetzwerk Mecklenburg-Vorpommern e. V. 1 Hanse- und Universitätsstadt Rostock – Büro der Oberbürgermeisterin 1 Landesbetrieb Immobilienmanagement und Grundvermögen 1 Name: author, dtype: int64
#display(df3.author.value_counts())
display(df3.owner_org.value_counts())
4309c328-c618-4077-bcdc-378a21ee1b46 2917
aa742a0e-7ba4-4f65-ab9f-c587dec73891 627
1172ac79-c506-44cc-9463-ae60d96f09f8 593
6c1b8763-a767-40a4-993c-fc515db2e28c 583
8bedecea-4a19-4807-8d58-59b83b4a6f92 505
...
0140936c-d4f9-4f5d-a6cb-929b90257cda 1
55e35d07-db62-4e97-89b3-c08d82b7ee68 1
645e9975-f9d4-48c7-b1c2-342fe6f9c94e 1
a4136229-3dc5-4353-947f-bcfdf8995735 1
fa7cf5ee-1353-4182-b679-4101afe66046 1
Name: owner_org, Length: 107, dtype: int64
display(df4.author.value_counts())
Fingal County Council 307 Open Data Liaison Officer 274 Helena Campbell 263 South Dublin County Council 155 Dublin City Council 99 Deirdre Kelly 75 Geological Survey of Ireland 45 Environmental Protection Agency 44 DLR GIS Officer 41 National Biodiversity Data Centre 29 Dún Laoghaire-Rathdown County Council 28 Not supplied 11 Data and Analytics Unit 10 Data Officer 7 Roscommon County Council 7 Data & Analytics Unit 5 Wexford County Council 4 Department of Arts, Heritage and the Gaeltacht 3 Donal McWeeney 3 DCC Transport City Centre Projects 2 Smart Dublin Team 2 info@smartdublin.ie 2 Smart Dublin 2 Oliver Dawkins 1 K Cooney 1 Nicola Graham 1 Dublin City Council 1 Richard Sheehan 1 Dublin City Council Culture Company 1 DCC 1 L Binns 1 The Property Registration Authority 1 GIS Officer 1 Sid Daly 1 Meath County Council 1 donna gartland 1 CSO 1 data@dublincity.ie 1 Department of Housing, Local Government and Heritage 1 Google, Aclima & Dublin City Council 1 National Transport Authority 1 DCC Air Quality Monitoring and Noise Control Unit 1 Name: author, dtype: int64
df3.head()
| Unnamed: 0 | owner_org | maintainer | issued | title_for_slug | qualified_relations | private | maintainer_email | num_tags | contact_points | keywords | temporals | id | metadata_created | conforms_to | metadata_modified | author | author_email | relations | state | version | relationships_as_object | license_id | type | resources | scheduled | num_resources | description | tags | title | language | groups | creator_user_id | display_name | relationships_as_subject | license_title | publisher | organization | name | isopen | accrual_periodicity | documentation | modified | url | see_alsos | revision_id | identifier | spatial | coverage | extras | FAIR_assessment | year_created | year_modified | F1A | F1B | F2A | F2B | A1.1 | A1.2 | I1 | I2 | I3 | R1.1 | R1.2 | R1.3 | Score | Country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | b3d5c5d8-f736-459e-b23b-fe0ea6196cf2 | Sara Schärrer | 2023-03-02T00:00:00 | Tierseuchenmeldungen in der Schweiz | [] | False | sara.schaerrer@blv.admin.ch | 9 | [{'name': 'Kompetenzzentrum Daten', 'email': '... | {'fr': ['animal', 'veterinaire', 'epizootie'],... | [{'start_date': '1991-01-01T00:00:00', 'end_da... | 702ddd5c-ca98-4a78-96ef-9aafdbea8f2d | 2023-05-05 06:30:17.188021 | [] | 2024-06-19 07:29:52.862215 | NaN | NaN | [{'url': 'https://www.fedlex.admin.ch/eli/cc/1... | active | NaN | [] | NaN | dataset | [{'documentation': [], 'url_type': None, 'cach... | NaN | 3 | {'fr': "La banque de données InfoSM (= système... | [{'vocabulary_id': None, 'state': 'active', 'd... | {'fr': 'Annonces des cas d‘épizootie en Suisse... | ['de', 'fr', 'it'] | [{'display_name': {'fr': 'Santé', 'de': 'Gesun... | 0594d621-c92a-4a32-809c-98e281dc7944 | {} | [] | NaN | {"url": "https://www.blv.admin.ch", "name": "B... | {'display_name': {'fr': 'Office fédéral de la ... | meldepflichtige-tierseuchen-in-der-schweiz | False | http://publications.europa.eu/resource/authori... | [] | NaN | https://infosm.blv.admin.ch/ | [] | c8793a79-b0ff-4e59-98bc-0dec70e90e88 | tierseuchen@bundesamt-fur-lebensmittelsicherhe... | NaN | NaN | NaN | [{'metric': 'F1A', 'score': '2'}, {'metric': '... | 2023 | 2024 | 2 | 0 | 1 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 9 | Switzerland |
| 1 | 25 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2023-08-30T00:00:00 | klimaanalyse-windgeschwindigkeit-nacht-zukunft... | [] | False | NaN | 0 | [{'email': 'afu@bd.so.ch', 'name': 'afu@bd.so.... | {'fr': [], 'de': ['klimaanalyse'], 'en': [], '... | [] | d2b483d0-838c-4a3b-b93e-16685da0b0f0 | 2024-06-19 05:37:57.613414 | [] | 2024-06-19 05:37:57.613423 | NaN | NaN | [{'url': 'https://www.geocat.ch/geonetwork/srv... | active | NaN | [] | NaN | dataset | [{'protocol': 'WWW:DOWNLOAD:GeoTIFF', 'cache_l... | NaN | 1 | {'fr': '', 'de': 'Windgeschwindigkeit um 4 Uhr... | [] | {'fr': '', 'de': 'Klimaanalyse - Windgeschwind... | ['de'] | [{'display_name': {'fr': 'Régions et villes', ... | af084126-f711-4016-a585-70354e997796 | {} | [] | NaN | {"url": "https://afu.so.ch", "name": "Amt f\u0... | {'display_name': {'fr': '', 'de': 'Kanton Solo... | klimaanalyse-windgeschwindigkeit-nacht-zukunft... | False | http://publications.europa.eu/resource/authori... | NaN | 2023-08-30T00:00:00 | https://data.geo.so.ch?filter=ch.so.afu.klimaa... | [] | 5d7ec063-fd29-4322-9c33-04bc607e72c1 | f5312f7d-dba5-4ca3-8419-de3fea19d6c7@kanton_so... | Kanton Solothurn (SO) | NaN | [{'key': 'harvest_object_id', 'value': '230dfa... | [{'metric': 'F1A', 'score': '2'}, {'metric': '... | 2024 | 2024 | 2 | 0 | 1 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 9 | Switzerland |
| 2 | 26 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2024-06-06T00:00:00 | gebaudeflachen-mit-adressen-amtliche-vermessung2 | [] | False | NaN | 0 | [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... | {'fr': [], 'de': ['gebaude', 'adresse', 'egid'... | [] | 0f3681b9-7508-421f-8f23-f482de9a8485 | 2024-06-19 05:34:29.756561 | [] | 2024-06-19 05:34:29.756569 | NaN | NaN | [{'url': 'https://www.geocat.ch/geonetwork/srv... | active | NaN | [] | NaN | dataset | [{'protocol': 'WWW:DOWNLOAD:DXF', 'cache_last_... | NaN | 4 | {'fr': '', 'de': 'Gebäudeflächen und Adressen ... | [] | {'fr': '', 'de': 'Gebäudeflächen mit Adressen ... | ['de'] | [{'display_name': {'fr': 'Régions et villes', ... | af084126-f711-4016-a585-70354e997796 | {} | [] | NaN | {"url": "https://agi.so.ch", "name": "Amt f\u0... | {'display_name': {'fr': '', 'de': 'Kanton Solo... | gebaudeflachen-mit-adressen-amtliche-vermessung2 | False | http://publications.europa.eu/resource/authori... | NaN | 2024-06-06T00:00:00 | https://data.geo.so.ch?filter=ch.so.agi.av.geb... | [] | cd1c1574-bab5-46a7-a55b-13d026cc857c | 1e984455-1980-4ba0-b87c-633b0971d99d@kanton_so... | Kanton Solothurn (SO) | NaN | [{'key': 'harvest_object_id', 'value': '10425c... | [{'metric': 'F1A', 'score': '2'}, {'metric': '... | 2024 | 2024 | 2 | 0 | 1 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 9 | Switzerland |
| 3 | 27 | 1e4cc1ec-05f2-4801-821e-492a8199dced | NaN | 2024-01-31T00:00:00 | kantonsstrassen1 | [] | False | NaN | 0 | [{'email': 'avt@bd.so.ch', 'name': 'avt@bd.so.... | {'fr': [], 'de': ['strasse', 'strassen', 'verk... | [] | fa77acc4-d44d-455b-a4b1-42c06824ff33 | 2024-06-19 05:33:30.010765 | [] | 2024-06-19 05:33:30.010773 | NaN | NaN | [{'url': 'https://www.geocat.ch/geonetwork/srv... | active | NaN | [] | NaN | dataset | [{'protocol': 'WWW:DOWNLOAD:DXF', 'cache_last_... | NaN | 4 | {'fr': '', 'de': 'Die Kantonsstrassen sind übe... | [] | {'fr': '', 'de': 'Kantonsstrassen', 'en': '', ... | ['de'] | [{'display_name': {'fr': 'Régions et villes', ... | af084126-f711-4016-a585-70354e997796 | {} | [] | NaN | {"url": "https://avt.so.ch", "name": "Amt f\u0... | {'display_name': {'fr': '', 'de': 'Kanton Solo... | kantonsstrassen1 | False | http://publications.europa.eu/resource/authori... | ['https://geo.so.ch/map?l=ch.so.avt.kantonsstr... | 2024-01-31T00:00:00 | https://data.geo.so.ch?filter=ch.so.avt.kanton... | [] | 4491b576-5897-4ea9-9f89-fcc121cec591 | 9209c1e4-6adf-452b-9870-235a6dbe01bb@kanton_so... | Kanton Solothurn (SO) | NaN | [{'key': 'harvest_object_id', 'value': 'd4e9a6... | [{'metric': 'F1A', 'score': '2'}, {'metric': '... | 2024 | 2024 | 2 | 0 | 1 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 9 | Switzerland |
| 4 | 29 | 51ffc9d5-bf18-4bb4-9b26-ad9b45a60e14 | Stadt Winterthur, Stadtentwicklung der Stadt W... | 2022-09-20T17:10:34+01:00 | beschaftigtendichte-in-der-stadt-winterthur-ab... | [] | False | statistik@win.ch | 4 | [{'email': 'statistik@win.ch', 'name': 'Stadt ... | {'fr': [], 'de': ['bevoelkerung', 'gemeinden',... | [{'start_date': '1995-01-01T00:00:00', 'end_da... | 86419980-f6af-462a-a672-27ed54f77299 | 2022-11-15 16:04:52.337827 | [] | 2024-06-19 04:12:54.928353 | NaN | NaN | [{'url': 'https://openzh.github.io/starter-cod... | active | NaN | [] | NaN | dataset | [{'byte_size': 10925, 'cache_last_updated': No... | NaN | 2 | {'fr': '', 'de': 'Anzahl und Anteil Beschäftig... | [{'vocabulary_id': None, 'state': 'active', 'd... | {'fr': '', 'de': 'Beschäftigtendichte in der S... | ['de'] | [] | af084126-f711-4016-a585-70354e997796 | {} | [] | NaN | {"url": "https://opendata.swiss/organization/s... | {'display_name': {'fr': '', 'de': 'Stadt Winte... | beschaftigtendichte-in-der-stadt-winterthur-ab... | False | http://publications.europa.eu/resource/authori... | [] | 2024-06-04T16:54:18+01:00 | https://stadt.winterthur.ch/themen/die-stadt/w... | [{'dataset_identifier': '1521@stadt-winterthur... | 449f19b9-7b35-4d6e-beeb-2f3492da35e6 | 1621@stadt-winterthur | NaN | NaN | [{'value': '1621@stadt-winterthur', 'key': 'gu... | [{'metric': 'F1A', 'score': '2'}, {'metric': '... | 2022 | 2024 | 2 | 0 | 1 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 9 | Switzerland |
combined_df.Score.value_counts()
4.0 17194 15.0 10849 9.0 6537 11.0 3851 5.0 3064 12.0 2800 16.0 719 21.0 303 24.0 280 19.0 264 0.0 169 17.0 145 13.0 144 2.0 103 7.0 30 6.0 29 8.0 24 20.0 23 10.0 15 22.0 6 14.0 4 18.0 4 1.0 1 3.0 1 Name: Score, dtype: int64
combined_df[fair_metrics].value_counts()
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3
2 0 0 0.0 2.0 0 0 0 0 0 0 0 17194
1 1.0 2.0 2 1 1 0 2 2 1 10752
0 1 1 0 0 0 1 6531
2 1 3249
0 0 1 1.0 0.0 0 1 1 0 0 0 1 3051
...
2 1 1
2 0 2 1 1
2 0 1 2.0 2.0 0 1 2 2 0 0 2 1
1.0 2.0 2 1 1 0 2 2 0 1
2.0 2.0 0 1 1 0 0 0 2 1
Length: 71, dtype: int64
df[fair_metrics].value_counts()
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3
2 0 1 1 2 2 1 1 0 2 2 1 10724
0 1 1 0 0 2 1 3243
0 0 0 0 0 0 0 0 0 0 0 0 74
2 0 1 1 2 2 1 1 2 2 2 1 44
2 1 1 2 2 1 1 2 2 2 1 40
0 1 1 0 0 2 1 7
1 1 1 2 2 1 1 2 2 2 1 2
0 2 1 2 2 2 1 2 2 2 1 1
1 1 1 2 2 1 1 0 2 2 1 1
2 1 1 2 0 1 1 2 0 2 1 1
dtype: int64
99% of the Austrian datasets can be described by two score-combinations (out of the multiple thousand potential ones), which suggests there's a strong framework/recommendation how these datasets should be published. (In total 10 unique combinations)
df4[fair_metrics].value_counts()
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3
2 0 0 0.0 2.0 0 0 0 0 0 0 0 10428
1 2.0 2.0 0 1 2 0 0 0 2 1331
1.0 2.0 0 1 1 0 0 0 1 1008
0 0 0 0.0 2.0 0 0 0 0 0 0 0 71
0.0 0 0 0 0 0 0 0 38
2 0 1 1.0 2.0 2 1 1 0 2 2 1 28
2 1 1.0 2.0 2 1 1 2 2 2 1 14
0 1 1.0 2.0 0 0 0 0 0 0 0 12
0 0.0 2.0 0 0 0 0 0 0 1 10
1 1.0 2.0 0 1 2 0 0 0 2 10
0 0.0 2.0 0 1 1 0 0 0 1 10
2 1 1.0 2.0 0 1 1 2 0 0 1 9
0 0 1 2.0 0.0 0 1 2 0 0 0 2 9
2 0 1 1.0 2.0 0 1 1 0 0 0 2 9
2.0 2.0 0 1 2 0 0 0 1 8
0 0 1 1.0 0.0 2 1 1 0 2 2 1 7
2 0 1 2.0 2.0 0 0 0 0 0 0 0 7
1.0 2.0 2 1 1 2 2 2 1 6
0 1 1 0 0 0 0 5
0 0 1 1.0 0.0 0 1 1 0 0 0 1 5
2 0 0 0.0 2.0 0 0 0 0 0 0 2 5
1 0.0 2.0 0 1 0 0 0 0 0 4
0 0.0 2.0 0 1 2 0 0 0 2 4
2 1 2.0 2.0 2 1 2 2 2 2 2 3
0 1 1.0 2.0 0 1 1 2 0 0 1 3
0 2 2 1 2
2 1 1.0 2.0 0 1 1 2 0 2 1 2
0.0 2.0 0 0 0 0 0 0 0 2
0 0 1 1.0 2.0 0 1 1 2 0 2 1 2
2 0 1 1.0 2.0 0 1 1 2 0 2 1 2
0 2.0 2.0 0 1 2 0 0 0 2 2
1 1.0 2.0 2 1 1 2 2 0 1 2
0.0 2.0 0 0 0 0 0 0 0 2
1 1 0 0 0 1 2
0 1.0 2.0 0 1 1 0 0 0 1 2
0.0 2.0 0 0 1 0 0 0 1 2
0 0 1 1.0 0.0 0 1 1 2 0 2 1 1
2 2 1 2.0 2.0 0 1 1 2 0 0 1 1
0 0 0 0.0 0.0 0 0 1 0 0 0 1 1
2 2 1 1.0 2.0 0 1 1 0 0 0 1 1
0.0 2.0 0 1 1 0 0 0 1 1
0 1 1.0 0.0 0 1 1 2 0 2 1 1
1 1 0.0 2.0 0 0 0 0 0 0 0 1
0 1 2.0 2.0 2 1 2 2 2 2 2 1
1.0 2.0 0 1 1 0 0 2 1 1
2.0 2.0 0 1 2 0 0 0 0 1
1 0 0 0 2 1
1 1
0 0 1 1.0 2.0 0 1 1 0 0 2 1 1
2 0 1 0.0 0.0 0 0 0 0 0 0 0 1
0 0 1 2.0 2.0 0 1 2 0 0 0 2 1
2 0 1 1.0 2.0 2 1 1 0 2 2 0 1
0 1 0 0 0 0 0 1
0 0 0 0.0 0.0 0 0 0 0 0 0 1 1
1 1.0 2.0 0 1 1 0 0 0 1 1
dtype: int64
Also for Ireland the majority of datasets only differ in very few principles, but there are a couple that take also different combinations.
df2[fair_metrics].value_counts()
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3
0 0 1 1 0 0 1 1 0 0 0 1 3035
2 0 1 1 2 0 1 1 0 0 0 1 2963
0 0 2 0 0 0 0 0 0 0 2805
1 2 2 2 1 2 0 0 2 2 268
0 0 0 0 0 0 0 0 0 0 0 0 3
dtype: int64
df3[fair_metrics].value_counts()
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3
2 0 0 0 2 0 0 0 0 0 0 0 3961
1 1 2 0 1 1 0 0 0 1 2560
2 2 0 1 2 0 0 0 2 1469
1 2 0 1 1 2 0 0 1 567
2 2 2 1 2 0 2 0 2 434
2 2 1 2 2 2 1 2 2 2 1 303
2 2 2 2 2 2 2 2 2 280
1 1 2 2 1 1 2 2 2 1 209
0 1 1 2 0 1 1 2 0 2 1 121
2 1 1 2 2 0 1 90
2 1 57
0 0 0 0 0 0 0 0 0 0 0 0 54
2 2 1 1 2 2 1 1 2 2 0 1 33
0 0 0 0 2 0 0 0 0 0 0 0 31
2 0 1 2 2 2 1 2 2 2 2 2 22
2 1 2 2 0 1 2 0 0 2 2 16
0 0 1 1 0 0 1 1 0 0 0 1 11
2 0 1 1 0 0 0 1 6
2 0 1 1 2 0 1 1 0 0 2 1 5
0 0 1 2 0 0 1 2 0 0 0 2 5
2 1 1 2 2 2 1 2 0 2 0 2 5
0 1 0 2 0 1 0 0 0 0 0 4
2 1 2 2 2 1 2 2 2 2 2 3
1 2 0 1 1 2 0 0 1 3
0 1 2 2 2 1 2 2 2 0 2 2
2 1 1 2 2 1 1 0 0 2 1 1
0 1 1 2 0 2 1 1
2 2 0 1 2 0 0 0 2 1
0 0 0 2 0 0 0 0 0 0 2 1
0 0 1 1 0 0 1 1 0 0 2 1 1
2 0 1 2 2 0 1 2 2 0 0 2 1
0 0 1 2 2 0 1 2 0 0 0 2 1
dtype: int64
df[fair_metrics].hist(bins=20, figsize=(20, 15), layout=(4, 3))
plt.suptitle('Histograms of FAIR principle scores Austria')
plt.show()
for i in fair_metrics:
display(combined_df[i].value_counts())
2 43200 0 3359 Name: F1A, dtype: int64
0 45617 2 933 1 9 Name: F1B, dtype: int64
1 28472 0 17503 2 584 Name: F2A, dtype: int64
1.0 25165 0.0 17516 2.0 3873 Name: F2B, dtype: int64
2.0 43311 0.0 3247 Name: A1.1, dtype: int64
0 33978 2 12581 Name: A1.2, dtype: int64
1 28462 0 17513 2 584 Name: I1, dtype: int64
1 25161 0 17519 2 3879 Name: I2, dtype: int64
0 44732 2 1827 Name: I3, dtype: int64
0 34245 2 12314 Name: R1.1, dtype: int64
0 31137 2 15422 Name: R1.2, dtype: int64
1 25164 0 17509 2 3886 Name: R1.3, dtype: int64
combined_df[fair_metrics].hist(bins=20, figsize=(20, 15), layout=(4, 3))
plt.suptitle('Histograms of FAIR principle scores all countries')
plt.show()
F1A(Unique IDs - FAIRChecker check that the resource identifier is an URL that can be reach, meaning it is unique, it is even better if the URL refer to a DOI) is the only principle that almost all datasets comply with - although its not surprising, as we used the URLs to perform the assessment
F1B(Persistent IDs - Weak : FAIR-Checker verifies that at least one namespace from identifiers.org is used in metadata. Strong : FAIR-Checker verifies that the “identifier” property from DCTerms or Schema.org vocabularies is present in metadata.) and I3(External links - FAIR-Checker verifies that at least 3 different URL authorities are used in the URIs of RDF metadata.) principles on the other hand are basically never adhered